I updated my list of turfgrass Shiny apps with the new GP avatar app from PACE Turf.
With just a few clicks, you can get a link to download a representation of the turfgrass growth potential at any location.
My favorite part of the app? The file names. We wrote the app to make sure the default file names include site information.
Turfgrass growth is affected by temperature. The temperature-based turfgrass growth potential (GP) is an easy way to look at how the actual temperature is related to the optimum temperatures for growth.
PACE Turf put together this GP avatar Shiny app to generate a simple plot of cool-season and warm-season grass GP.
There are two options with this app. One can automatically generate avatars for thousands of locations in the USA based on ZIP codes, or one can enter the temperatures for any location.
For more information about GP and how it can be used, see:
I went for a run to the ocean at Cerro Gordo. As is my wont, I looked for grasses along the way. There were lots of zoysia lawns where I started. Then I got on a trail and headed down to the water.
When I stopped along the ocean's edge, I enjoyed the view, but I didn't see any zoysia. I was reminded of the rocky shores of the islands in Okinawa. But in Okinawa, one will find lots of zoysia growing on the rocks and cliffs.
This is on the western tip of Ishigaki Island. Lots of zoysia growing wild. Why is the zoysia growing wild in the East China Sea, South China Sea, and Philippine Sea, but more in maintained turf areas in the Caribbean Sea?
I think the answer lies in the combination of light and temperature. Specifically, locations with a longer duration of time at high temperature combined with low light will have a prevalence of zoysia. These locations may also have zoysia that grows faster than bermuda (Cynodon) or paspalum.
I looked up the combination of temperature and sunshine hours on this customizable chart.
Locations on the chart to the right are hotter, and lower on the chart have less sunshine. The "trails" for each location trace the normal combination of temperature and sunshine for an entire year.
I looked up cumulative precipitation too. This should have an effect too, although for competition between species in managed turf, precipitation should be less important, because irrigation can be supplied.
I'd like to grow different species of grass in representative climates and measure how much they grow. I expect certain species would map to location, somewhat like the locations are separated in the chart below by temperature and light.
On a visit to Manila some time ago, I visited a golf course and had a look around with the golf course superintendent. Our discussion turned to seasonal changes in weather and the relative impact on the grass. For example, in the winter, when the temperatures are a little cooler, is it lower temperatures that have a big impact on slowing the growth? Or might it be the light, because the days in winter are a little shorter and the sun is a little lower in the sky?
I made some calculations then, and reran the script now to refresh my memory about this.
I calculated what the photosynthetic light would be on a sunny day in each month.
That's how the photosynthetic photon flux density (PPFD) will be throughout the year when there are no clouds. It varies a bit.
For the total light per day we can look at the daily light integral (DLI), and this is how the DLI changes through the year.
The PPFD is how much light reaches the turf every second. Adding together the light from each second, from sunrise until sunset, gives the total amount for the day -- the DLI.
What about temperature? Manila is 13 degrees north of the equator with a tropical climate. The coldest month is January, with an average temperature of 25.6°C. The hottest month is May, with an average temperature of 29.5°C. That's a difference of 3.9°C from the coldest to the hottest month.
The sunny day DLI ranges from about 44 to 59 mol m-2 d-1. So which changes more, the temperature, or the light?
To look at that, I plotted the standard score (z-score) for the DLI and for the temperature across 12 months. The z-score shows how many standard deviations a value is from the average (mean).
If the z-score is less than 0, that means the value for that month is less than the average for the year. If the z-score is more than 0, that means the value for that month is more than the average for the year.
Because the z-scores are standardized, I can compare directly the DLI and the temperature, and how much they are changing in any one month, compared to their average value during the year.
At Manila on sunny days, the temperature varies relatively more than the light in January, February, April, and May. In March, and from June through December, the light varies relatively more than the temperature.
Over the past two weeks, I've had multiple conversations about the way I think of turfgrass management. It all starts with a definition of greenkeeping as managing the growth rate of the grass. I wrote about this in A Short Grammar of Greenkeeping. You can get your copy here.
Application of the grammar allows for easy communication among turfgrass managers about the work they are doing. I'll use the creeping bentgrass greens at Hazeltine National GC as an example. Volunteers from near and far were at Hazeltine during the Ryder Cup.
The International Stimpmeter crew. @asianturfgrass @Reyes_golf @drumcturf #RyderCup2016 pic.twitter.com/COeVFis46h
— Jeff Johnson (@MinikahdaTurf) September 27, 2016
Let's say that I was from Madrid, or San Francisco, or Sydney, and I wanted to get green conditions that were more like those at Hazeltine. One of the ways I would try to do that would be to apply a similar quantity of nitrogen. But how to compare locations?
I would use the temperature-based growth potential (GP). For Minneapolis, the GP looks like this.
If I set the maximum monthly N at 3 g/m2, and multiply by the GP, I get a maximum annual N of 13.3 g/m2 for that location (Minneapolis). Now I'll make up a number, because I don't know exactly what it is, but let's say the actual quantity of N applied at Hazeltine was 9 g/m2.
I'll use the log percentage (L%) difference for consistency. The L% is the natural logarithm of the ratio of two numbers, multiplied by 100:
If 9 g N were applied at Hazeltine, and the value calculated using GP as described above is 13.3 g, that is a 39 L% reduction.
If I want to apply proportionally the same amount of N at another location, I can calculate the GP amount, which I'll call a standard value, and then take a 39 L% reduction.
The standard using these calculations comes to 16.7 g at Madrid, 20.1 at San Francisco, and 28.9 at Sydney. Knowing that there was a 39 L% reduction at Hazeltine, my starting point for Madrid, after applying the same reduction, would be 11.3 g N/m2. At San Francisco, the N would go from the standard calculation of 20.1 down to 13.6 g, and at Sydney the 39 L% reduction takes N from 28.9 to 19.6.
This grammar facilitates the rapid sharing of relative inputs used to produce turf surfaces all over the world. Let's say we know there are amazing bentgrass greens in Sydney with N inputs of 10 g/m2/year. A corresponding quantity of N in Minneapolis would be 4.6 g.
This same approach can be applied for the quantity of water supplied in comparison to evapotranspiration (ET), to frequency of mowing, to evaluation of the growth rate, to assessment of the photosynthetic light, and so on. I find this approach quite useful in rapid implementation of maintenance practices that work well at location A, applied to location B. One then has a site specific starting point that can be further adjusted at location B, based on turfgrass response at that location.
I made a Shiny app with climatological normals data from the Japan Meteorological Agency to show the combination of sunshine and temperature at 11 locations.
@naturalgolf_D asked "What kind of situation is Japan?" With these data, I think it is interesting to compare different locations of interest, and a Shiny app is an easy way to do that.
Interesting‼︎ What kind of situation is Japan? https://t.co/DK3EeOcEQl
— natural golf design (@naturalgolf_D) September 12, 2016
Six more Shiny apps from ATC are here.
On July 17, I was in the Tokyo area with Jim Brosnan. The daily light integral (DLI) in Tokyo on July 17 was 14.2 mol/m2. Jim asked me if it was exceptionally cloudy that day. Not really, I answered. I told him that such cloudiness was normal.
Now that July 2016 is over, I looked at the DLI for every day in July at Tokyo and also at Batesville, Arkansas. Both are at about 35.7°N latitude, so the day lengths will be identical.
The lowest DLI at Batesville in July was 22.8 mol/m2 on July 29. In Tokyo, there were 10 days in July with a DLI less than 22.8 mol/m2, including 5 days with a DLI less than 10 mol/m2. In that context, the cloudiness on July 17 was not exceptional.
To see more, check out the average hourly PPFD and DLI values for Tokyo in this chart and for Batesville in this one.
Mike Richardson pointed out that the growth rate of zoysia is less than bermuda, so by implication there must be something other than growth rate that allows zoysia to invade bermuda. That is, in the situations when bermuda and zoysia are growing together -- competing -- when zoysia appears to grow faster, Mike suggests it may be a factor such as turf density that allows such a result, because bermuda grows faster than zoysia.
I've outlined a hypothesis about grass growth rates and their required inputs, and have more to write about that later. In that hypothesis, I mention location, and in my recent discussion with Mike about the growth rate I said that there is a variety by climate interaction. By climate, I mean the same as location. I'll use these words interchangeably.
Let me try to explain what I mean by an interaction by climate. I'll use data from Tokyo, and from Batesville (2016 data) and Fort Smith (climatological normals data). These locations are all about 35°N.
Light, temperature, plant water status, and leaf nitrogen content all influence growth. In turfgrass management, light and temperature generally can't be controlled; plant water status and leaf nitrogen content can be modified by turfgrass managers. We can imagine that bermuda and zoysia are growing side by side, or together, and then think of what may happen with modifications to these growth-influencing factors.
On average, this is the part of the climate that can't be controlled, at Fort Smith and at Tokyo, shown in 2-dimensional space.
That's a similar temperature range but different amounts of sunshine. Thus, there is no overlap during the months when warm-season grasses are growing. I focus on light and temperature because the water and the nitrogen can be adjusted by the turf manager.
Temperatures for 2016 are pretty similar through July 30. I express temperature here as the cumulative sum of growing degree days.
Ok, so temperatures are similar. If it were only temperature that influences growth, one would expect the grasses to perform pretty much the same at these locations. If bermuda does have an inherently faster growth rate than zoysia, then in this side-by-side comparison, with the same temperature, then bermuda should grow faster at both locations.
I downloaded the global solar radiation data also and then converted it into photosynthetic radiation units. This is Batesville for the first 7 months of 2016.
This is Tokyo for the first 7 months of 2016.
In 2016, there has been more photosynthetic light at Batesville than at Tokyo.
The DLI was pretty much the same from January to March, but since the start of April Batesville has jumped ahead by about 1,000 moles/m2. In the past 4 months, Tokyo has accumulated about 4,000 mol/m2 and Batesville has accumulated about 5,000 mol/m2. That's a log percentage difference of 22%. The difference has been especially pronounced in June and July -- the hottest months of the year so far.
Imagine growing bermuda and zoysia in 10% shade at the same temperature. Bermuda may grow faster than zoysia. Now imagine 20% shade. Probably the same result. How about 30, 40, and 50% shade? 60% or 70% shade? At some point, the growth rate of zoysia will be greater than the growth rate of bermuda. The bermuda will die in shade under which the zoysia can still produce a turf.
Consider now that there are varying growth rates among bermudagrass varieties, and also among zoysia varieties. That's what I mean by the location (or climate) by variety interaction. Take an inherently faster-growing zoysia, mix it with bermuda, grow it in a climate with high temperatures combined with lower DLI, mow the grass and make sure plenty of water is applied during the dry season, and see which one grows faster. It's not bermuda.
Yes, with a high DLI, plenty of fertilizer, moderate water supply, and high temperatures, bermuda grows faster than zoysia. Here's a photo of the ATC research facility putting green during grow-in. It's easy to tell which plots are zoysia -- those closest to the camera.
But if one thinks of growth as something that happens over years, at a location, with the grasses maintained as turf, then one can find the growth rate of zoysia can be higher than that of bermuda.
I find it useful to look at growth rate in those terms, rather than trying to explain it as a response to density or as competition for some other factor.
Plotting the normal temperatures and sunshine hours for a location places that location in a particular 2-dimensional space. I demonstrated that in these charts. @turfstuf suggested that a diagonal line might show a break point for classifying warm and cool-season grasses.
@asianturfgrass Looks like a blurry line drawn from 14h to 35C might show an over/under for some warm/cool grasses & growing seasons.
— turfstuf (@turfstuf) April 16, 2016
The idea is that the top right would be warm-season, the area around the line would be transition zone, and the area to the bottom left would be cool season. That chart looks like this.
I agree that different regions of the chart are indicative of over/under points for different grasses or growing conditions. I wouldn't separate by that diagonal line. Here's the break points I would use.
For those general breaks, one can estimate the annual mean from the monthly charts, or plot the locations by the mean annual temperature.
Continuing with the breaks, specifically looking at which warm-season grasses will be suitable:
A transition zone location like Atlanta looks like this when those points are marked on the plot.
Two warm-season locations, one where bermuda thrives (Honolulu) and another where bermuda is overgrown by more shade tolerant grasses (Hilo), are shown here.
In the next plots I show some other locations: cool-season, warm-season, and transition zone. The break points I use seem to agree pretty well with grass distribution and performance around the world.
Of the factors that influence plant growth, turfgrass managers are able to modify in some way the plant water status and the nitrogen supply to the grass, but they can do little to adjust the temperature and the light. As a consequence, both the grass adaptation to a particular environment, and the management requirements for the grass, will be influenced or controlled by the combination of light and temperature.
I spoke about this at a conference in 2012 and shared this handout. From the start of the handout:
The weather, and specifically the temperature and the amount of sunshine, has a major influence on the growth of grass and therefore on the suitability of certain grasses for certain climates. By plotting the climatological normal weather data with temperature on the horizontal axis (x-axis) and sunshine hours on the vertical axis (y-axis), we can see which locations are similar in these parameters, and thus likely to be suitable for the same grasses, and to similar maintenance practices for grasses. Many locations in East, South, and Southeast Asia are distinguished by relatively low sunshine duration as compared with locations of similar temperature in North America, Oceania, Africa, and Europe. For additional information about the use of these charts, see www.climate.asianturfgrass.com.
The idea is that when temperature and sunshine are the same (or similar) at two or more locations, the growing conditions, and the energy available for grass growth, are the same (or similar). When the temperature and sunshine are different, with no overlap, then the growing conditions are clearly different.
I think this is interesting and informative because such an approach can help to identify places that we might think are similar, but are in fact different, and vice versa. The implications for maintenance requirements, grass selection, and location to location comparisons are also evident from such representations of climate data. I've made some more plots to illustrate this.
I start with Miami. The normal monthly mean temperature is shown on the x-axis and the mean daily sunshine hours for that month are shown on the y-axis. The polygon defined by each of the 12 months of the year expresses what the normal growing environment is like at Miami. Places that are similar to Miami should have overlap in light and temperature with Miami. Places that are different should have little or no overlap.
Moscow, for example, has no overlap with Miami. I don't think anyone would expect it to.
The hottest months of the year at Moscow are cooler (with more sunshine) than the coldest months at Miami. There is no overlap between these locations.
New York City has some overlap with both Miami and Moscow. If I plot New York on this chart, I can see which months at New York are similar to Miami or Moscow.
June in New York is similar in temperature and sunshine to March in Miami, September in New York is almost the same as January in Miami, and July and August in New York are between March and October conditions in Miami. One can also see the seasonal overlap between New York and Moscow conditions.
How about another warm season location like Miami? This plot adds Singapore conditions.
There is no overlap between Singapore and Miami, even though both are warm-season locations. There is more overlap between New York and Miami (about 3 months) than there is between Singapore and Miami (0 months). This has implications for grass selection and management. That is, the grasses the work in Singapore may not do very well in Miami, and vice versa.
Some places are predictably similar. Portland, and Seattle, for example, have almost complete overlap.
Other locations that one might expect to be similar have no overlap at all. I often use Honolulu and Hilo as an example. And sure enough, one finds different grass species growing at these two locations.
This video discusses Hilo and Honolulu.
One can also look at transition zone locations, like Atlanta, where both warm and cool-season grasses are grown.
How does a cool-season location like London compare to Atlanta?
June and July in London are similar to March and November in Atlanta. July and August in London have similar temperatures as October in Atlanta, but with less sunshine.
Melbourne is another transition zone location, with golf course fairways and sports fields usually planted to warm-season grasses, and golf course putting greens usually planted to cool-season grasses.
Melbourne has some overlap with both Atlanta and London.